This course covers problem-solving approaches in data analysis, including descriptive, predictive, and prescriptive analysis. It also covers software tools selection, testing approaches, and approach selection, with tips and best practices.

To help technology professionals advance their career in data science and analytics, this course explores conceptual models, how to build, verify, run, and evaluate them before they can be calibrated with the available data in the organization.

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

Describe model frameworks, their benefits and limitations, and features to help optimize your Power BI data models.

Power Query has an incredible number of features that are dedicated to helping you clean and prepare your data for analysis. You'll learn how to simplify a complicated model, change data types, rename objects, and pivot data. You'll also learn how to profile columns so that you know which columns have the valuable data that you're seeking for deeper analytics.

This course teaches optimizing Power Query in Power BI to load, cleanse, and transform data, which is essential in the PL-300 certification exam.

Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.

Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.

Learn to clean and manipulate data using the Pandas library in Python. Cover common issues like missing values and irrelevant features, use correlation analysis, encode categorical features, and prepare data for machine learning models.

Learn how to clean data with Apache Spark in Python.

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.